We consider fronthaul-limited generalized zero-forcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multiple-antenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral efficiency (SE) performance of the system wherein the users use the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. Specifically, we formulate an optimization problem for the user association and power control for maximizing the sum SE. The formulated problem is under per-AP transmit power and fronthaul constraints, and is based on only long-term channel state information (CSI). The challenging formulated problem is transformed into tractable form and a novel algorithm is proposed to solve it using minorization maximization (MM) technique. We analyze the trade-offs provided by the CF-mMIMO system with different number of CB clusters, hence highlighting the importance of the appropriate choice of CB design for different system setups. Numerical results show that for the centralized CB, the proposed power optimization provides nearly 59% improvement in the average sum SE over the heuristic approach, and 312% improvement, when the distributed beamforming is employed.

This paper studies a cell-free massive multiple-input multiple-output (CF-mMIMO) proactive monitoring system in which multiple multi-antenna monitoring nodes (MNs) are assigned to either observe the transmissions from an untrusted transmitter (UT) or to jam the reception at the untrusted receiver (UR). We propose an effective channel state information (CSI) acquisition scheme for the monitoring system. In our approach, the MNs leverage the pilot signals transmitted during the uplink and downlink phases of the untrusted link and estimate the effective channels corresponding to the UT and UR via a minimum mean-squared error (MMSE) estimation scheme. We derive new spectral efficiency (SE) expressions for the untrusted link and the monitoring system. For the latter, the SE is derived for two CSI availability cases at the central processing unit (CPU); namely case-1: imperfect CSI knowledge at both MNs and CPU, case-2: imperfect CSI knowledge at the MNs and no CSI knowledge at the CPU. To improve the monitoring performance, we propose a novel joint mode assignment and jamming power control optimization method to maximize the monitoring success probability (MSP) based on the Bayesian optimization framework. Numerical results show that (a) our CF-mMIMO proactive monitoring system relying on the proposed CSI acquisition and optimization approach significantly outperforms the considered benchmarks; (b) the MSP performance of our CF-mMIMO proactive monitoring system is greater than 0.8, regardless of the number of antennas at the untrusted nodes or the precoding scheme for the untrusted transmission link.

Integrated sensing and communication (ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by malicious foes. In this paper, we propose a new wireless proactive monitoring paradigm, where a legitimate monitor intercepts a suspicious communication link while performing cognitive jamming to enhance the monitoring success probability (MSP) and simultaneously safeguard the target. To this end, we derive closed-form expressions of the signal-to-interference-plus-noise-ratio (SINR) at the user (UE), sensing access points (S-APs), and an approximating expression of the SINR at the proactive monitor. Moreover, we propose an optimization technique under which the legitimate monitor minimizes the success detection probability (SDP) of the legitimate target, by optimizing the jamming power allocation over both communication and sensing channels subject to total power constraints and monitoring performance requirement. To enhance the monitor’s longevity and reduce the risk of detection by malicious ISAC systems, we further propose an adaptive power allocation scheme aimed at minimizing the total transmit power at the monitor while meeting a pre-selected sensing SINR threshold and ensuring successful monitoring. Our numerical results show that the proposed algorithm significantly compromises the sensing and communication performance of malicious ISAC.

In this paper, we exploit the cell-free massive multiple-input multiple-output (CF-mMIMO) architecture to design a physical-layer authentication (PLA) framework that can simultaneously authenticate multiple distributed users across the coverage area. Our proposed scheme remains effective even in the presence of active adversaries attempting impersonation attacks to disrupt the authentication process. Specifically, we introduce a tag-based PLA CF-mMIMO system, wherein the access points (APs) first estimate their channels with the legitimate users during an uplink training phase. Subsequently, a unique secret key is generated and securely shared between each user and the APs. We then formulate a hypothesis testing problem and derive a closed-form expression for the probability of detection for each user in the network. Numerical results validate the effectiveness of the proposed approach, demonstrating that it maintains a high detection probability even as the number of users in the system increases.

Joint unicast and multicast transmissions are becoming increasingly important in practical wireless systems, such as Internet of Things networks. This paper investigates a cell-free massive multiple-input multiple-output system that simultaneously supports both transmission types, with multicast serving multiple groups. Exact closed-form expressions for the achievable downlink spectral efficiency (SE) of both unicast and multicast users are derived for zero-forcing and maximum ratio precoding designs. Accordingly, a weighted sum SE (SSE) maximization problem is formulated to jointly optimize the access point (AP) selection and power allocation. The optimization framework accounts for practical constraints, including the maximum transmit power per AP, fronthaul capacity limitations between APs and the central processing unit, and quality-of-service requirements for all users. The resulting non-convex optimization problem is reformulated into a tractable structure, and an accelerated projected gradient (APG)-based algorithm is developed to efficiently obtain near-optimal solutions. As a performance benchmark, a successive convex approximation (SCA)-based algorithm is also implemented. Simulation results demonstrate that the proposed joint optimization approach significantly enhances the SSE across various system setups and precoding strategies. In particular, the APG-based algorithm achieves substantial complexity reduction while maintaining competitive performance, making it well-suited for large-scale practical deployments.

We present an overview of ongoing research endeavors focused on in-band full-duplex (IBFD) massive multiple-input multiple-output (MIMO) systems and their applications. In response to the unprecedented demands for mobile traffic in concurrent and upcoming wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems becomes imperative. Cell-free massive MIMO (CF-mMIMO) emerges as a practical and scalable implementation of distributed/network MIMO systems, serving as a crucial physical layer technology for the advancement of next-generation wireless networks. This architecture inherits benefits from co-located massive MIMO and distributed systems and provides the flexibility for integration with the IBFD technology. We delineate the evolutionary trajectory of cellular networks, transitioning from conventional half-duplex multi-user MIMO networks to IBFD CF-mMIMO. The discussion extends further to the emerging paradigm of network-assisted IBFD CF-mMIMO (NAFD CF-mMIMO), serving as an energy-efficient prototype for asymmetric uplink and downlink communication services. This novel approach finds applications in dual-functionality scenarios, including simultaneous wireless power and information transmission, wireless surveillance, and integrated sensing and communications. We highlight various current use case applications, discuss open challenges, and outline future research directions aimed at fully realizing the potential of NAFD CF-mMIMO systems to meet the evolving demands of future wireless networks.

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization problems are solved using successive convex approximation (SCA) techniques. The proposed framework design is further applied to the average sum-HE maximization and energy harvesting fairness problems. Our numerical results demonstrate that the proposed joint AP operation mode selection and power control algorithm can achieve EE performance gains of up to 4-fold and 5-fold over random AP operation mode selection, with and without power control respectively.

In this paper, we investigate joint power control and AP selection scheme in a cell-free massive multiple-input multiple-output (CF-mMIMIO) system under an active eaves-dropping attack, where an eavesdropper tries to overhear the signal sent to one of the legitimate users by contaminating the uplink channel estimation. We formulate a joint optimization problem to minimize the eavesdropping spectral efficiency (SE) while guaranteeing a given SE requirement at legitimate users. The challenging formulated problem is converted into a more tractable form and an efficient low-complexity accelerated pro-jected gradient (APG)-based approach is proposed to solve it. Our findings reveal that the proposed joint optimization approach significantly outperforms the heuristic approaches in terms of secrecy SE (SSE). For instance, the 50% likely SSE performance of the proposed approach is 265 % higher than that of equal power allocation and random AP selection scheme.

A cell-free massive multiple-input-multiple-output (CF-mMIMO) system is considered for enhancing the monitoring performance of wireless surveillance, where a large number of distributed multiantenna aided legitimate monitoring nodes (MNs) proactively monitor multiple distributed untrusted communication links. We consider two types of MNs whose task is to either observe the untrusted transmitters or jam the untrusted receivers. We first analyze the performance of CF-mMIMO surveillance relying on both maximum ratio (MR) and partial zero-forcing (PZF) combining schemes and derive closed-form expressions for the monitoring success probability (MSP) of the MNs. We then propose a joint optimization technique that designs the MN mode assignment, power control, and MN-weighting coefficient control to enhance the MSP based on the long-term statistical channel state information knowledge. This challenging problem is effectively transformed into tractable forms and efficient algorithms are proposed for solving them. Numerical results show that our proposed CF-mMIMO surveillance system considerably improves the monitoring performance with respect to a full-duplex co-located massive multiple-input-multiple-output (MIMO) proactive monitoring system. More particularly, when the untrusted pairs are distributed over a wide area and use the MR combining, the proposed solution provides nearly a thirty-fold improvement in the minimum MSP over the co-located massive MIMO baseline, and forty-fold improvement, when the PZF combining is employed.

To meet the unprecedented mobile traffic demands of future wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems is imperative. Cell-free massive multiple-input multiple-output (CF-mMIMO) represents a practical and scalable embodiment of distributed/network MIMO systems. It inherits not only the key benefits of co-located massive MIMO systems but also the macro-diversity gains from distributed systems. This innovative architecture has demonstrated significant potential in enhancing network performance from various perspectives, outperforming co-located mMIMO and conventional small-cell systems. Moreover, CF-mMIMO offers flexibility in integration with emerging wireless technologies such as full-duplex (FD), nonorthogonal transmission schemes, millimeter-wave (mmWave) communications, ultrareliable low-latency communication (URLLC), unmanned aerial vehicle (UAV)-aided communication, and reconfigurable intelligent surfaces (RISs). In this article, we provide an overview of current research efforts on CF-mMIMO systems and their promising future application scenarios. We then elaborate on new requirements for CF-mMIMO networks in the context of these technological breakthroughs. We also present several current open challenges and outline future research directions aimed at fully realizing the potential of CF-mMIMO systems in meeting the evolving demands of future wireless networks.

We study secure communications in cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multi-antenna access points (APs) and protective partial zero-forcing (PPZF) precoding. In particular, we consider an active eavesdropping attack, where an eavesdropper contaminates the uplink channel estimation phase by sending an identical pilot sequence with a legitimate user of interest. We formulate an optimization problem for maximizing the received signal-to-noise ratio (SINR) at the legitimate user, subject to a maximum allowable SINR at the eavesdropper and maximum transmit power at each AP, while guaranteeing specific SINR requirements on other legitimate users. The optimization problem is solved using a path-following algorithm. We also propose a large-scale-based greedy AP selection scheme to improve the secrecy spectral efficiency (SSE). Finally, we propose a simple method for identifying the presence of an eavesdropper within the system. Our findings show that PPZF can substantially outperform the conventional maximum-ratio transmission (MRT) scheme by providing around 2-fold improvement in the SSE compared to the MRT scheme. More importantly, for PPZF precoding scheme, our proposed AP selection can achieve a remarkable SSE gain of up to 220%, while our power optimization approach can provide an additional gain of up to 55% compared with a CF-mMIMO system with equal power allocation.

Wireless surveillance, in which untrusted communications links are proactively monitored by legitimate agencies, has started to garner a lot of interest for enhancing the national security. In this paper, we propose a new cell-free massive multiple-input multiple-output (CF-mMIMO) wireless surveillance system, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) embark on either observing or jamming untrusted communication links. To facilitate concurrent observing and jamming, a subset of the MNs is selected for monitoring the untrusted transmitters (UTs), while the remaining MNs are selected for jamming the untrusted receivers (URs). We analyze the performance of CF-mMIMO wireless surveillance and derive a closed-form expression for the monitoring success probability of MNs. We then propose a greedy algorithm for the observing vs, jamming mode assignment of MNs, followed by the conception of a jamming transmit power allocation algorithm for maximizing the minimum monitoring success probability concerning all the UT and UR pairs based on the associated long-term channel state information knowledge. In conclusion, our proposed CF-mMIMO system is capable of significantly improving the performance of the MNs compared to that of the state-of-the-art baseline. In scenarios of a mediocre number of MNs, our proposed scheme provides an 11-fold improvement in the minimum monitoring success probability compared to its colocated mMIMO benchmarker.

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems incorporating simultane-ous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. To optimize both the spectral efficiency (SE) of IUs and harvested energy (HE) of EUs, we propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. We investigate the problem of maximizing the total HE for EUs, considering constraints on SE for individual IUs and minimum HE for individual EUs. Our numerical results showcase that the proposed AP operation mode selection algorithm can provide up to 76% and 130% performance gains over random AP operation mode selection with and without power control, respectively,

We consider a reflecting intelligent surface (RIS)-assisted massive multiple-input multiple-output (MIMO) system to facilitate simultaneous wireless information and power transfer (SWIPT) towards two groups of information users (IUs) and energy users (EUs) over Rician fading channels. By considering partial zero-forcing (PZF) precoding at the base station (BS), we derive closed-from expressions for the achievable downlink spectral efficiency (SE) of the IUs and average harvested energy at the EUs. Our results rigorously demonstrate the impact of various system parameters on the actual performance. We next propose a maxmin fairness transmit power allocation which seeks to maximize the minimum harvested power by EUs, subject to quality-of-service constraints at all IUs, relying on statistical channel state information (CSI) and a realistic non-linear energy harvesting (EH) model for the EUs. Our numerical results reveal that the interplay between the RIS and massive MIMO can significantly boost the performance of SWIPT in wireless networks.

We consider a cell-free massive MIMO (CF-mMIMO) system with multi-antenna access points (APs) and distributed protective partial zero-forcing (PPZF) precoding, which is prone to an active eavesdropping attack during uplink training. We develop a tractable analytical framework to derive a novel closed-form expression for the spectral efficiency (SE) at the users and eavesdropper (Eve), and, hence, the secrecy SE. These closed-form expressions are of particular importance for enabling further system design. Our findings show that PPZF can substantially outperform the conventional maximum-ratio transmission (MRT) scheme especially when the ratio of number of AP antennas to the number of users is high. Moreover, the secrecy enhancement obtained by using a higher number of AP antennas is more pronounced when Eve is located farther away from the legitimate user. Finally, simulation results validate the accuracy of the derived theoretical analysis.

In this paper, we address the antenna selection (AS) problem in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay bridges the connection between the multi-antenna base station and NOMA far user. Specifically, two AS schemes, namely max-U1 and max-U2, are proposed to maximize the end-to-end signal-to-interference-plus-noise ratio at either or both near and far users, respectively. Moreover, a two-stage AS scheme, namely quality-of-service (QoS) provisioning scheme, is designed to realize a specific rate at the far user while improving the near user’s rate. To enhance the performance of the QoS provisioning AS scheme, the idea of dynamic antenna clustering is applied at the relay to adaptively partition the relay’s antennas into transmit and receive subsets. The proposed AS schemes’ exact outage probability and achievable rate expressions are derived. To provide more insight, closed-form asymptotic outage probability expressions for the max-U1 and max-U2 AS schemes are obtained. Our results show that while the QoS provisioning AS scheme can deliver a near-optimal performance for static antenna setup at the relay, it provides up to 12% average sum rate gain over the optimum AS selection with fixed antenna setup.

This paper investigates the application of full-duplex (FD) multi-antenna transceivers in proactive eavesdropping systems. To this end, we jointly optimize the transmit and receive beamformers at the legitimate FD monitor to maximize the eavesdropping non-outage probability of the system. The resulting non-convex problem is solved using two-layer decomposition technique. The inner layer problem is formulated as a semidefinite relaxation problem, and the outer problem is solved by one-dimensional line search. We further propose sub-optimum beamforming designs, where the beamformers are obtained using zero-forcing, and maximum ratio transmission. To archive a low-complexity implementation, we study the antenna selection problem as an alternative for performance optimization. Particularly, based on the system's eavesdropping non-outage probability, several antenna selection schemes are proposed to choose single transmit and single receive antenna at the FD monitor. For each scheme, we derive closed-form expressions of the eavesdropping non-outage probability. Our findings reveal that proposed antenna selection schemes can achieve the performance close to that of the proposed optimum/sub-optimum beamforming design, but with much lower implementation complexity.

Wireless energy harvesting, physical-layer security, and full-duplex wireless are important, emerging fifth generation (5G) technologies. In this paper, we thus investigate a source-destination link with an energy-harvesting full-duplex relay and a jammer (to degrade the eavesdropper channel) in the presence of an eavesdropper. Thus, to exploit energy harvesting and to improve security, we propose a full-duplex jammer (FDJ) protocol and its half-duplex version (HDJ). Two cases for availability of the eavesdropper channel state information (ECSI) are considered: complete ECSI and incomplete ECSI. For both FDJ and HDJ protocols and for complete ECSI, we derive the instantaneous and average secrecy rates and compute optimal time split for energy harvesting. To gain more insights, we consider a practical interference-limited scenario and derive closed-form cumulative distribution function of the signal-to-interference plus noise ratio at the destination and eavesdropper nodes. Comparatively, we show that FDJ improves instantaneous secrecy rate over HDJ. However, the degree of improvement is highly dependent on time split for energy harvesting, amount of self-interference, the channel gains, and locations of the nodes. Our findings reveal that FDJ increases the average secrecy rate 150% over HDJ and 260% over HD relaying without jammer. For incomplete ECSI scenario, we derive asymptotic secrecy outage and show that FDJ performs better for small-to-medium values of source powers; otherwise, HDJ yields a higher gain.

We consider downlink non-orthogonal multiple access transmission where an access point communicates with a set of near and far users via a full-duplex multiple antenna relay. To deal with the inter-user interference at the near user and self-interference at the relay, we propose the optimum and suboptimal beamforming schemes. In addition, we consider two different user selection criteria, namely: 1) random near user and random far user (RNRF) selection and 2) nearest near user and nearest far user (NNNF) selection, and we derive the outage probabilities of the near and far users. Our findings reveal that as compared to half-duplex operation, full-duplex relaying can reduce the outage probability of the near users up to 63% in the case of NNNF user selection. With suboptimal beamforming schemes, the NNNF user selection shows a superior performance as compared to the RNRF user selection for all choices of transmit power, while with the optimum beamforming, the performance of the RNRF user selection converges to the NNNF user selection at high transmit power. The simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

We investigate the problem of antenna selection (AS) in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay assists transmission from a multi-antenna base station (BS) to a far user, while at the same, the BS transmits to a near user. Specifically, based on the end-to-end signal-to-interference-plus-noise ratio at the near and far users, two AS schemes to select a single transmit antenna at both the BS and the relay, respectively, as well as a single receive antenna at relay are proposed. In order to study the ergodic sum rate and outage probability of these AS schemes, we have derived closed-form expressions assuming Rayleigh fading channels. The sum rate and outage probability of the AS schemes are also compared with the optimum selection scheme that maximizes the performance as well as with a random AS scheme. Our results show that the proposed AS schemes can deliver a near-optimal performance for near and far users, respectively.

In this paper, we consider a non-orthogonal multiple access cognitive radio network, where a full-duplex (FD) multi-antenna relay assists transmission from an access point (AP) to a cognitive far user, while at the same time, the AP transmits to a cognitive near user. Our objective is to maximize the rate of the near user under a constraint that the rate of the far user is above a certain threshold. To this end, a non-convex joint optimization problem of relay beamforming and the transmit powers at the AP and FD relay is solved as a semi-definite relaxation problem, in conjunction with an efficiently solvable line-search approach. We also consider a low complexity fixed beamformer design, where the optimum power allocation between the AP and FD relay is solved. Several fixed beamforming designs based on the zero-forcing criterion are proposed for which exact and asymptotic outage probability expressions corresponding to the near and far users are derived. Our results demonstrate that the proposed joint optimization can significantly reduce the self-interference impact at the FD relay and inter-user interference in the near user case.

We consider a cooperative non-orthogonal multiple access (NOMA) network in which a full-duplex (FD) multi-antenna relay assists transmission from a base station (BS) to a set of far users with poor channel conditions, while at the same time the BS transmits to a set of near users with strong channel conditions. We assume imperfect self- interference (SI) cancellation at the FD relay and imperfect inter-user interference cancellation at the near users. In order to cancel the SI at the relay a zero-forcing based beamforming scheme is used and the corresponding outage probability analysis of two user selection strategies, namely random near user and random far user (RNRF), and nearest near user and nearest far user (NNNF), are derived. Our finding suggests that significant performance improvement can be achieved by using the FD multi- antenna relay compared to the counterpart system with a half-duplex relay. The achieved performance gain depends on network parameters such as the user density, user zones, path loss and the strength of the inter-user interference in case of near users. We also show that the NNNF strategy exhibits a superior outage performance compared to the RNRF strategy, especially in the case of near user.

We analyze the maximum gain that can be achieved through cooperative broadcast with energy accumulation and memory. We consider a linear network, where a known number of nodes are placed on a line, and derive an upper bound on Gtot, the gain of cooperative broadcast over noncooperative broadcast with respect to total power consumption. Specifically, we prove that, in linear networks with path loss exponent α = 2, Gtot≤ π2/12-π2≈ 4.64, irrespective of the number of devices, the size of the network, the node placement strategy, and the cooperative broadcast strategy. We extend this result to any path loss exponent α ≥ 2. We also show that the cooperation gain in shortrange transmissions, wherein the circuit energy consumption is nonnegligible, is smaller than that in long-range transmissions. We further study the cooperative broadcast gain when the objective is to reduce the maximum transmission power used by any node in the network. In this case, we show that, when nodes are distributed uniformly at random, the maximum cooperation gain will be O(log n), with high probability, where n is the number of nodes in the network. These are important observations that should be considered in designing power-efficient broadcast algorithms in future network-wide cooperative broadcast.

Physical-layer network coding (PNC) promises a significant gain In overall network throughput for multi-user cooperative communications. In a multi-unicast scenario, one drawback associated with PNC is an additional noise term, coined as network coding (NC) noise, which severely degrades the system data rate. In this paper, our contribution to address this challenging problem is source and relay power control with the objective of maximization of the minimum average achievable rate among all the source-destination pairs subject to a given total power constraint. We further develop a joint power allocation and relay selection scheme, which only rely on long-term channel statistics, to extend our results to general network topologies. We show that the joint optimization problem can be divided into two problems: optimal power allocation and optimal relay selection where the former is scalable and leads to a power assignment algorithm that exhibits the same optimization complexity for any number of sources in the network and the latter can be performed in a decentralized manner. By means of simulations, we validate our theoretical developments and verify the efficiency of our algorithm in improving the average achievable rate compared to a multi-unicast system with no power control or relay selection. We conclude that the proposed algorithm largely combats the adverse effects of NC noise while achieving near optimal fairness.

Network coding (NC) is known as a promising approach to improve the cooperative communication network throughput. However, in certain situations, it can introduce additional noise terms which is recently referred to as NC noise. We consider such a problem in a two-unicast wireless system and seek to answer the following question: “Can we reduce or remove network coding noise by proper power allocation at the relay?” To this end, we provide a mathematical framework for the output signal-to-noise (SNR) ratio and instantaneous sum-rate of the network-coded cooperative communication (NC-CC) system with the notion of power assignment at the relay. Based on this framework, we provide two novel closed-form power allocation techniques that are suitable for slow and fast fading conditions. Numerical analysis is used to confirm the accuracy of the derived theory and to show the effectiveness of proposed solutions in terms of average sum-rate and outage probability. It is shown that such techniques offer a significant advantage in overcoming the adverse effects of NC noise, especially in slow fading, without introducing significant extra costs or system complexity.

Order acceptance decisions have a significant influence on the short- and long-term performance of a firm. Firms, therefore, should make complex order acceptance decisions with regard to their organizational goals in an effective way. Models are called for to provide insight for decision-makers in developing proper strategies for order acceptance. This paper presents an agent-based model to support order acceptance decisions in a multi-plant enterprise, particularly incorporating buyer-seller negotiations. The negotiation process is conceptualized in three phases, pre-negotiation, negotiation, and post-negotiation, taking into account salient viewpoints on making order acceptance decisions. The conceptualization and the developed model not only provide valuable insights into the nature of negotiation in a supply chain setting, but also contribute to the understanding of strategy development for order acceptance.